OpenRaiser/Intern-Atlas huggingface dataset:
Effects of Co-channel Interference on RIS Empowered Wireless Networks amid Multiple Eavesdropping Attempts
Venue: arXiv cs.IT 2023
Year: 2023

This letter is concerned with the secrecy performance of reconfigurable intelligent surfaces (RIS)-aided wireless networks in the existence of multiple interferers towards the destination. To be more precise, we analyze three critical issues in the design of secure RIS-assisted networks: 1) How do interferers affect the performance of secure wireless networks? 2) Which of the two groups of eavesdroppers (i.e., colluding and non-colluding) is more severe? 3) How can RIS improve network confidentiality? To do so, we develop the analytical expression of secrecy outage probability in closed-form, along with asymptotic analysis at high signal-to-noise ratio regime to better understand the impacts of different system parameters on secrecy performance. Finally, we validate our analytical results using a computer based Monte-Carlo simulation.
CQnet: convex-geometric interpretation and constraining neural-network trajectories
Venue: arXiv cs.LG 2023
Year: 2023

We introduce CQnet, a neural network with origins in the CQ algorithm for solving convex split-feasibility problems and forward-backward splitting. CQnet's trajectories are interpretable as particles that are tracking a changing constraint set via its point-to-set distance function while being elements of another constraint set at every layer. More than just a convex-geometric interpretation, CQnet accommodates learned and deterministic constraints that may be sample or data-specific and are satisfied by every layer and the output. Furthermore, the states in CQnet progress toward another constraint set at every layer. We provide proof of stability/nonexpansiveness with minimal assumptions. The combination of constraint handling and stability put forward CQnet as a candidate for various tasks where prior knowledge exists on the network states or output.
Trajectory-User Linking via Hierarchical Spatio-Temporal Attention Networks
Venue: arXiv cs.LG 2023
Year: 2023

Trajectory-User Linking (TUL) is crucial for human mobility modeling by linking diferent trajectories to users with the exploration of complex mobility patterns. Existing works mainly rely on the recurrent neural framework to encode the temporal dependencies in trajectories, have fall short in capturing spatial-temporal global context for TUL prediction. To ill this gap, this work presents a new hierarchical spatio-temporal attention neural network, called AttnTUL, to jointly encode the local trajectory transitional patterns and global spatial dependencies for TUL. Speciically, our irst model component is built over the graph neural architecture to preserve the local and global context and enhance the representation paradigm of geographical regions and user trajectories. Additionally, a hierarchically structured attention network is designed to simultaneously encode the intra-trajectory and inter-trajectory dependencies, with the integration of the temporal attention mechanism and global elastic attentional encoder. Extensive experiments demonstrate the superiority of our AttnTUL method as compared to state-of-the-art baselines on various trajectory datasets. The source code of our model is available at https://github.com/Onedean/AttnTUL.
Scientific Computing with Diffractive Optical Neural Networks
Venue: arXiv cs.LG 2023
Year: 2023

Diffractive optical neural networks (DONNs) have been emerging as a high-throughput and energy-efficient hardware platform to perform all-optical machine learning (ML) in machine vision systems. However, the current demonstrated applications of DONNs are largely straightforward image classification tasks, which undermines the prospect of developing and utilizing such hardware for other ML applications. Here, we numerically and experimentally demonstrate the deployment of an all-optical reconfigurable DONNs system for scientific computing, including guiding two-dimensional quantum material synthesis, predicting the properties of nanomaterials and small molecular cancer drugs, predicting the device response of nanopatterned integrated photonic power splitters, and the dynamic stabilization of an inverted pendulum with reinforcement learning. Despite a large variety of input data structures, we develop a universal feature engineering approach to convert categorical input features to the images that can be processed in the DONNs system. Our results open up new opportunities of employing DONNs systems for a broad range of ML applications.
Deep Neural Networks for Encrypted Inference with TFHE
Venue: arXiv cs.LG 2023
Year: 2023

Fully homomorphic encryption (FHE) is an encryption method that allows to perform computation on encrypted data, without decryption. FHE preserves the privacy of the users of online services that handle sensitive data, such as health data, biometrics, credit scores and other personal information. A common way to provide a valuable service on such data is through machine learning and, at this time, Neural Networks are the dominant machine learning model for unstructured data. In this work we show how to construct Deep Neural Networks (DNN) that are compatible with the constraints of TFHE, an FHE scheme that allows arbitrary depth computation circuits. We discuss the constraints and show the architecture of DNNs for two computer vision tasks. We benchmark the architectures using the Concrete stack, an open-source implementation of TFHE.
Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action
Venue: arXiv cs.LG 2023
Year: 2023

Denoising diffusion models have emerged as one of the most powerful generative models in recent years. They have achieved remarkable success in many fields, such as computer vision, natural language processing (NLP), and bioinformatics. Although there are a few excellent reviews on diffusion models and their applications in computer vision and NLP, there is a lack of an overview of their applications in bioinformatics. This review aims to provide a rather thorough overview of the applications of diffusion models in bioinformatics to aid their further development in bioinformatics and computational biology. We start with an introduction of the key concepts and theoretical foundations of three cornerstone diffusion modeling frameworks (denoising diffusion probabilistic models, noise-conditioned scoring networks, and stochastic differential equations), followed by a comprehensive description of diffusion models employed in the different domains of bioinformatics, including cryo-EM data enhancement, single-cell data analysis, protein design and generation, drug and small molecule design, and protein-ligand interaction. The review is concluded with a summary of the potential new development and applications of diffusion models in bioinformatics.
Generative Oversampling for Imbalanced Data via Majority-Guided VAE
Venue: arXiv cs.LG 2023
Year: 2023

Learning with imbalanced data is a challenging problem in deep learning. Over-sampling is a widely used technique to re-balance the sampling distribution of training data. However, most existing over-sampling methods only use intra-class information of minority classes to augment the data but ignore the inter-class relationships with the majority ones, which is prone to overfitting, especially when the imbalance ratio is large. To address this issue, we propose a novel over-sampling model, called Majority-Guided VAE~(MGVAE), which generates new minority samples under the guidance of a majority-based prior. In this way, the newly generated minority samples can inherit the diversity and richness of the majority ones, thus mitigating overfitting in downstream tasks. Furthermore, to prevent model collapse under limited data, we first pre-train MGVAE on sufficient majority samples and then fine-tune based on minority samples with Elastic Weight Consolidation(EWC) regularization. Experimental results on benchmark image datasets and real-world tabular data show that MGVAE achieves competitive improvements over other over-sampling methods in downstream classification tasks, demonstrating the effectiveness of our method.
Balanced Audiovisual Dataset for Imbalance Analysis
Venue: arXiv cs.LG 2023
Year: 2023

The imbalance problem is widespread in the field of machine learning, which also exists in multimodal learning areas caused by the intrinsic discrepancy between modalities of samples. Recent works have attempted to solve the modality imbalance problem from algorithm perspective, however, they do not fully analyze the influence of modality bias in datasets. Concretely, existing multimodal datasets are usually collected under specific tasks, where one modality tends to perform better than other ones in most conditions. In this work, to comprehensively explore the influence of modality bias, we first split existing datasets into different subsets by estimating sample-wise modality discrepancy. We surprisingly find that: the multimodal models with existing imbalance algorithms consistently perform worse than the unimodal one on specific subsets, in accordance with the modality bias. To further explore the influence of modality bias and analyze the effectiveness of existing imbalance algorithms, we build a balanced audiovisual dataset, with uniformly distributed modality discrepancy over the whole dataset. We then conduct extensive experiments to re-evaluate existing imbalance algorithms and draw some interesting findings: existing algorithms only provide a compromise between modalities and suffer from the large modality discrepancy of samples. We hope that these findings could facilitate future research on the modality imbalance problem.
'The Taurus': Cattle Breeds & Diseases Identification Mobile Application using Machine Learning
Venue: arXiv cs.LG 2023
Year: 2023

Dairy farming plays an important role in agriculture for thousands of years not only in Sri Lanka but also in so many other countries. When it comes to dairy farming cattle is an indispensable animal. According to the literature surveys almost 3.9 million cattle and calves die in a year due to different types of diseases. The causes of diseases are mainly bacteria, parasites, fungi, chemical poisons and etc. Infectious diseases can be a greatest threat to livestock health. The mortality rate of cattle causes a huge impact on social, economic and environmental damage. In order to decrease this negative impact, the proposal implements a cross-platform mobile application to easily analyze and identify the diseases which cattle suffer from and give them a solution and also to identify the cattle breeds. The mobile application is designed to identify the breeds by analyzing the images of the cattle and identify diseases after analyzing the videos and the images of affected areas. Then make a model to identify the weight and the age of a particular cow and suggest the best dose of the medicine to the identified disease. This will be a huge advantage to farmers as well as to dairy industry. The name of the proposed mobile application is 'The Taurus' and this paper address the selected machine learning and image processing models and the approaches taken to identify the diseases, breeds and suggest the prevention methods and medicine to the identified disease.
A Reinforcement Learning Framework for Online Speaker Diarization
Venue: arXiv cs.SD 2023
Year: 2023

Speaker diarization is a task to label an audio or video recording with the identity of the speaker at each given time stamp. In this work, we propose a novel machine learning framework to conduct real-time multi-speaker diarization and recognition without prior registration and pretraining in a fully online and reinforcement learning setting. Our framework combines embedding extraction, clustering, and resegmentation into the same problem as an online decision-making problem. We discuss practical considerations and advanced techniques such as the offline reinforcement learning, semi-supervision, and domain adaptation to address the challenges of limited training data and out-of-distribution environments. Our approach considers speaker diarization as a fully online learning problem of the speaker recognition task, where the agent receives no pretraining from any training set before deployment, and learns to detect speaker identity on the fly through reward feedbacks. The paradigm of the reinforcement learning approach to speaker diarization presents an adaptive, lightweight, and generalizable system that is useful for multi-user teleconferences, where many people might come and go without extensive pre-registration ahead of time. Lastly, we provide a desktop application that uses our proposed approach as a proof of concept. To the best of our knowledge, this is the first approach to apply a reinforcement learning approach to the speaker diarization task.
Towards a responsible machine learning approach to identify forced labor in fisheries
Venue: arXiv cs.LG 2023
Year: 2023

Many fishing vessels use forced labor, but identifying vessels that engage in this practice is challenging because few are regularly inspected. We developed a positive-unlabeled learning algorithm using vessel characteristics and movement patterns to estimate an upper bound of the number of positive cases of forced labor, with the goal of helping make accurate, responsible, and fair decisions. 89% of the reported cases of forced labor were correctly classified as positive (recall) while 98% of the vessels certified as having decent working conditions were correctly classified as negative. The recall was high for vessels from different regions using different gears, except for trawlers. We found that as much as ~28% of vessels may operate using forced labor, with the fraction much higher in squid jiggers and longlines. This model could inform risk-based port inspections as part of a broader monitoring, control, and surveillance regime to reduce forced labor. * Translated versions of the English title and abstract are available in five languages in S1 Text: Spanish, French, Simplified Chinese, Traditional Chinese, and Indonesian.
Counterfeit Chip Detection using Scattering Parameter Analysis
Venue: arXiv cs.CR 2023
Year: 2023

The increase in the number of counterfeit and recycled microelectronic chips in recent years has created significant security and safety concerns in various applications. Hence, detecting such counterfeit chips in electronic systems is critical before deployment in the field. Unfortunately, the conventional verification tools using physical inspection and side-channel methods are costly, unscalable, error-prone, and often incompatible with legacy systems. This paper introduces a generic non-invasive and low-cost counterfeit chip detection based on characterizing the impedance of the system's power delivery network (PDN). Our method relies on the fact that the impedance of the counterfeit and recycled chips differs from the genuine ones. To sense such impedance variations confidently, we deploy scattering parameters, frequently used for impedance characterization of RF/microwave circuits. Our proposed approach can directly be applied to soldered chips on the system's PCB and does not require any modifications on the legacy systems. To validate our claims, we perform extensive measurements on genuine and aged samples from two families of STMicroelectronics chips to assess the effectiveness of the proposed approach.
Preventing Catastrophic Forgetting in Continual Learning of New Natural Language Tasks
Venue: arXiv cs.CL 2023
Year: 2023

Multi-Task Learning (MTL) is widely-accepted in Natural Language Processing as a standard technique for learning multiple related tasks in one model. Training an MTL model requires having the training data for all tasks available at the same time. As systems usually evolve over time, (e.g., to support new functionalities), adding a new task to an existing MTL model usually requires retraining the model from scratch on all the tasks and this can be time-consuming and computationally expensive. Moreover, in some scenarios, the data used to train the original training may be no longer available, for example, due to storage or privacy concerns. In this paper, we approach the problem of incrementally expanding MTL models' capability to solve new tasks over time by distilling the knowledge of an already trained model on n tasks into a new one for solving n+1 tasks. To avoid catastrophic forgetting, we propose to exploit unlabeled data from the same distributions of the old tasks. Our experiments on publicly available benchmarks show that such a technique dramatically benefits the distillation by preserving the already acquired knowledge (i.e., preventing up to 20% performance drops on old tasks) while obtaining good performance on the incrementally added tasks. Further, we also show that our approach is beneficial in practical settings by using data from a leading voice assistant.
Learning Mixture Structure on Multi-Source Time Series for Probabilistic Forecasting
Venue: arXiv cs.LG 2023
Year: 2023

In many data-driven applications, collecting data from different sources is increasingly desirable for enhancing performance. In this paper, we are interested in the problem of probabilistic forecasting with multi-source time series. We propose a neural mixture structure-based probability model for learning different predictive relations and their adaptive combinations from multi-source time series. We present the prediction and uncertainty quantification methods that apply to different distributions of target variables. Additionally, given the imbalanced and unstable behaviors observed during the direct training of the proposed mixture model, we develop a phased learning method and provide a theoretical analysis. In experimental evaluations, the mixture model trained by the phased learning exhibits competitive performance on both point and probabilistic prediction metrics. Meanwhile, the proposed uncertainty conditioned error suggests the potential of the mixture model's uncertainty score as a reliability indicator of predictions.
On the Design of Artificial Noise for Physical Layer Security in Visible Light Communication Channels with Clipping
Venue: arXiv cs.IT 2023
Year: 2023

Though visible light communication (VLC) systems are contained to a given room, improving their security is an important criterion in any practical deployment. In this paper, the design of artificial noise (AN) to enhance physical layer security in VLC systems is studied in the context of input signals with no explicit amplitude constraint (e.g., multicarrier systems). In such systems, clipping is needed to constrain the input signals within the limited linear ranges of the LEDs. However, this clipping process gives rise to non-linear clipping distortion, which must be incorporated into the AN design. To facilitate the solution of this problem, a sub-optimal design approach is presented using the Charnes-Cooper transformation and the convex-concave procedure (CCP). Then, a novel AN transmission scheme is proposed to reduce the impact of clipping distortion, thus improving the secrecy performance. The proposed scheme exploits the common structure of LED luminaries that they are often composed of several light-emitting chips. Capitalizing on this property, LED chips in each luminaire are divided into two groups driven by separate driver circuits. One group is used to transmit the information-bearing signal, while the other group transmits the AN. Numerical results show that the clipping distortion significantly reduces the secrecy level, and using AN is advantageous over the no-AN scheme in improving the secrecy performance. Moreover, the proposed AN transmission scheme is shown to achieve considerable secrecy improvements compared with the traditional transmission approaches (e.g., about 1 bit/s/Hz improvement in the achievable secrecy rate when the standard deviation of the LEDs' modulating current is 0.25 A and the signal-to-interference-plus-noise ratio of the eavesdropper's received signal is limited to $0$ dB).
An EEG-based Experiment on VR Sickness and Postural Instability While Walking in Virtual Environments
Venue: arXiv cs.HC 2023
Year: 2023

Previous studies showed that natural walking reduces the susceptibility to VR sickness. However, many users still experience VR sickness when wearing VR headsets that allow free walking in room-scale spaces. This paper studies VR sickness and postural instability while the user walks in an immersive virtual environment using an electroencephalogram (EEG) headset and a full-body motion capture system. The experiment induced VR sickness by gradually increasing the translation gain beyond the user's detection threshold. A between-group comparison between participants with and without VR sickness symptoms found some significant differences in postural stability but found none on gait patterns during the walking. In the EEG analysis, the group with VR sickness showed a reduction of alpha power, a phenomenon previously linked to a higher workload and efforts to maintain postural control. In contrast, the group without VR sickness exhibited brain activities linked to fine cognitive-motor control. The EEG result provides new insights into the postural instability theory: participants with VR sickness could maintain their postural stability at the cost of a higher cognitive workload. Our result also indicates that the analysis of lower-frequency power could complement behavioural data for continuous VR sickness detection in both stationary and mobile VR setups.
DISCO: Distributed Inference with Sparse Communications
Venue: arXiv cs.CV 2023
Year: 2023

Deep neural networks (DNNs) have great potential to solve many real-world problems, but they usually require an extensive amount of computation and memory. It is of great difficulty to deploy a large DNN model to a single resource-limited device with small memory capacity. Distributed computing is a common approach to reduce single-node memory consumption and to accelerate the inference of DNN models. In this paper, we explore the "within-layer model parallelism", which distributes the inference of each layer into multiple nodes. In this way, the memory requirement can be distributed to many nodes, making it possible to use several edge devices to infer a large DNN model. Due to the dependency within each layer, data communications between nodes during this parallel inference can be a bottleneck when the communication bandwidth is limited. We propose a framework to train DNN models for Distributed Inference with Sparse Communications (DISCO). We convert the problem of selecting which subset of data to transmit between nodes into a model optimization problem, and derive models with both computation and communication reduction when each layer is inferred on multiple nodes. We show the benefit of the DISCO framework on a variety of CV tasks such as image classification, object detection, semantic segmentation, and image super resolution. The corresponding models include important DNN building blocks such as convolutions and transformers. For example, each layer of a ResNet-50 model can be distributively inferred across two nodes with five times less data communications, almost half overall computations and half memory requirement for a single node, and achieve comparable accuracy to the original ResNet-50 model. This also results in 4.7 times overall inference speedup.